Spatiotemporal Derivative Pattern: A Dynamic Texture Descriptor for Video Matching

2015 ◽  
pp. 626-641
Author(s):  
Farshid Hajati ◽  
Mohammad Tavakolian ◽  
Soheila Gheisari ◽  
Ajmal Saeed Mian
Keyword(s):  
Dynamic Texture ◽  
Texture Descriptor ◽  
Video Matching

scholarly journals Wavelet-Based Dynamic Texture Classification Using Gumbel Distribution

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Yu-Long Qiao ◽  
Chun-Yan Song ◽  
Fu-Shan Wang
Keyword(s):  
Wavelet Transform ◽  
Texture Classification ◽  
Gumbel Distribution ◽  
Classification Problem ◽  
Classification Performance ◽  
Experimental Results ◽  
Dynamic Texture ◽  
Texture Descriptor ◽  
Complex Wavelet Transform

Dynamic texture classification has attracted growing attention. Characterization of a dynamic texture is vital to address the classification problem. This paper proposes a dynamic texture descriptor based on the dual-tree complex wavelet transform and the Gumbel distribution. The method takes out the median values of coefficient magnitudes in each nonoverlapping block of a detail subband and models them with the Gumbel distribution. The classification is realized by comparing the similarity between the estimated distributions of all detail subbands. The experimental results on the benchmark dynamic texture database demonstrate better histogram fitting and promising classification performance of the dynamic texture descriptor compared with the current existing methods.

scholarly journals Multi-scale characterizations of colon polyps via computed tomographic colonography

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Weiguo Cao ◽  
Marc J. Pomeroy ◽  
Yongfeng Gao ◽  
Matthew A. Barish ◽  
Almas F. Abbasi ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Texture Features ◽  
Learning Model ◽  
Colon Polyps ◽  
Texture Descriptor ◽  
Operating Characteristics ◽  
Computed Tomographic ◽  
Multi Scale ◽  
Definition Of ◽  
Occurrence Matrix

AbstractTexture features have played an essential role in the field of medical imaging for computer-aided diagnosis. The gray-level co-occurrence matrix (GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these applications. This study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture descriptor. In this study, we first introduce a new parameter - stride, to explore the definition of GLCM. Then we propose three multi-scaling GLCM models according to its three parameters, (1) learning model by multiple displacements, (2) learning model by multiple strides (LMS), and (3) learning model by multiple angles. These models increase the texture information by introducing more texture patterns and mitigate direction sparsity and dense sampling problems presented in the traditional Haralick model. To further analyze the three parameters, we test the three models by performing classification on a dataset of 63 large polyp masses obtained from computed tomography colonoscopy consisting of 32 adenocarcinomas and 31 benign adenomas. Finally, the proposed methods are compared to several typical GLCM-texture descriptors and one deep learning model. LMS obtains the highest performance and enhances the prediction power to 0.9450 with standard deviation 0.0285 by area under the curve of receiver operating characteristics score which is a significant improvement.

Empirical Evaluation of Generic Weighted Cubicle Pattern and LBP Derivatives for Abnormality Detection in Mammogram Images

2015 ◽  
Vol 15 (01) ◽  
pp. 1550001 ◽  
Author(s):  
A. Suruliandi ◽  
G. Murugeswari ◽  
P. Arockia Jansi Rani
Keyword(s):  
Image Segmentation ◽  
Performance Metrics ◽  
Empirical Evaluation ◽  
Rate Sensitivity ◽  
Digital Mammogram ◽  
Abnormality Detection ◽  
Texture Descriptor ◽  
Texture Pattern ◽  
Local Texture ◽  
Mammogram Images

Digital image processing techniques are very useful in abnormality detection in digital mammogram images. Nowadays, texture-based image segmentation of digital mammogram images is very popular due to its better accuracy and precision. Local binary pattern (LBP) descriptor has attracted many researchers working in the field of texture analysis of digital images. Because of its success, many texture descriptors have been introduced as variants of LBP. In this work, we propose a novel texture descriptor called generic weighted cubicle pattern (GWCP) and we analyzed the proposed operator for texture image classification. We also performed abnormality detection through mammogram image segmentation using k-Nearest Neighbors (KNN) algorithm and compared the performance of the proposed texture descriptor with LBP and other variants of LBP namely local ternary pattern (LTPT), extended local texture pattern (ELTP) and local texture pattern (LTPS). For evaluation, we used the performance metrics such as accuracy, error rate, sensitivity, specificity, under estimation fraction and over estimation fraction. The results prove that the proposed method outperforms other descriptors in terms of abnormality detection in mammogram images.

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